The application of machine learning and multi-source panel data is an example of how old and new technologies can make a big impact and create new opportunities in the Market Research industry.

A few months ago, I wrote the first part of this article, “Online Panels – The Black Sheep of Market Research?, and all the positive reaction and feedback were very inspiring and overwhelming. So, in this second part, I’d like to explore why I believe panel companies have the background, knowledge, tools and technologies that can help online market research be great again, as well as how they can “turn the table” and lead innovation in this industry.

Building and managing online panels for so many years, I’ve felt on my own skin the pains and frustrations of panel companies with bad surveys they need to fulfill. In our case for example, we have tried to be creative and tested several ways to improve UX, from developing research games, to building 3D worlds where respondents can participate with their avatars, to designing beautiful survey templates, and, more recently, applying Gamification techniques. Regardless, the years have proven that all of those methods still need a vital part in order to improve the user experience: how well the questionnaire is written and designed.

It has been a difficult mission to make our clients write user-friendly surveys, or take UX as a priority; however we have learned a lot by listening and collecting feedback from our users. And then we realized we could go far beyond delivering consumer data just based on surveys but increasingly from the data spontaneously shared by the users, including their experiences with products and brands, as well as their mobile and social data. The more data our panelists share, the greater the potential to apply such data to extract consumer intelligence, as well as to improve user experiences with market research. That is where Machine Learning, Deep Learning and AI can play a key role in understanding consumers, and also helping improve surveys. See a few examples below:

Based on users’ profiles, interests and behavior, panels don’t need to keep sending users repeated questions and surveys. They can use existing data and ML algorithms to respond to known questions and just ask the needed ones;

Using data shared by the users on their experiences with products and brands (eg: product reviews and customer service satisfaction) , panels can apply ML methods and deliver brand KPIs, product preferences, etc.

Panel companies have extensive profiling data about their members, from socio-demographics to thematic profiles on health, travel, technology, etc. By combining such data with users’ own social media data (eg: Facebook or Twitter), panels can provide to market researchers insights they will never get by just analyzing public social media;

Using historic survey data and users’ input, from both researchers and panelists, panels can create “smart surveys”, that is, surveys that improve over time, recommending the right questions to the right audiences.

Well, the application of machine learning and multi-source panel data is an example of how old and new technologies can make a big impact and create new opportunities in the Market Research industry. As commented before, I still believe in a bright future for online research and panels, at least the ones who are taking the right steps now. Am I dreaming too high?

@Justin, don’t want to mention names here, but the panel companies that I’d recommend are the ones who: 1) build a trust relationship with their panel members, valuing their time and prioritizing a superior user experience; 2) have strong technology capability (eg: advanced panel management platform and recruitment capacity for continuous panel refreshment); 3) have extensive market research background, applying quality control procedures from recruitment to data delivery. I believe this article (http://greenbookblog.org/2016/05/16/look-whos-talking-part-1-who-are-the-most-frequently-mentioned-research-panels/) has a good overview on small X large panel companies in the US. I’d be happy to discuss and provide more feedback and names based on my own experience via inbox (https://www.linkedin.com/in/adricrocha or adriana_rocha at ecglobal.com)